I know that most of you are not familiar with Bayes Theorem, but I think that it has application here. Bayes was a preacher in England and wanted to see if he could use mathematics to prove there was a God. He was unsuccessfully, but he developed an important mathematical and logical "theorem" in the process. I am going to try to apply his theorem to see I can shed some light on our recent Supreme Court decision.
First, I want to focus on assumptions or estimates that I will eventually try to use to apply Bayes Theorem too. If you do not agree with my basic assumptions, you can make your own and then apply Bayes Theorem and see what you end up with. We know that most decisions are right or wrong by degrees. Few are exact. If I were to ask if you thought I weighed more than 195# and you said "yes", what are the chances that your decision is correct? Is it 100%, or 90% or something else. Even a jury can not be 100% sure that their decision is correct. Surprisingly, Bayes Theorem provides a way to calculate those probabilities.
My first estimate is that a typical group made up of people who claim to have been raped, 85% have actually been raped and the other 15% are either lying or have a false memory. This is our starting group. If you were to pick a person randomly from this group, the person would look like the above. In the case of Dr. Ford, she was "not" selected randomly and thus may not match up with the above.
My second estimate is that Dr. Ford is less likely to be in the 85% group. My new estimate is that it is no better than 50% that she is in the raped group. I think that you would have to agree that she was not randomly selected and thus the probabilities are different from the original group.
We have yet another probability to consider and that is that if Dr. Ford was raped was it Judge Kavanough? My estimate is that the odds are 90% no and 10% yes.
First, I want to focus on assumptions or estimates that I will eventually try to use to apply Bayes Theorem too. If you do not agree with my basic assumptions, you can make your own and then apply Bayes Theorem and see what you end up with. We know that most decisions are right or wrong by degrees. Few are exact. If I were to ask if you thought I weighed more than 195# and you said "yes", what are the chances that your decision is correct? Is it 100%, or 90% or something else. Even a jury can not be 100% sure that their decision is correct. Surprisingly, Bayes Theorem provides a way to calculate those probabilities.
My first estimate is that a typical group made up of people who claim to have been raped, 85% have actually been raped and the other 15% are either lying or have a false memory. This is our starting group. If you were to pick a person randomly from this group, the person would look like the above. In the case of Dr. Ford, she was "not" selected randomly and thus may not match up with the above.
My second estimate is that Dr. Ford is less likely to be in the 85% group. My new estimate is that it is no better than 50% that she is in the raped group. I think that you would have to agree that she was not randomly selected and thus the probabilities are different from the original group.
We have yet another probability to consider and that is that if Dr. Ford was raped was it Judge Kavanough? My estimate is that the odds are 90% no and 10% yes.
